def CreateDataLoader(opt):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
data_loader.initialize(opt)
return data_loader
def CreateDataLoader(opt):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
print(data_loader.name())
data_loader.initialize(opt)
#print data_loader
return data_loader
def CreateDataLoader(opt):
''' Calls CustomDatasetDataLoader and initializes it. '''
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
print(data_loader.name())
data_loader.initialize(opt)
return data_loader
def CreateDataLoader(datafolder, dataroot='./dataset', dataset_mode='2afc',
load_size=64, batch_size=1, serial_batches=True,
nThreads=4):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
# print(data_loader.name())
data_loader.initialize(datafolder, dataroot=dataroot + '/' + dataset_mode,
dataset_mode=dataset_mode, load_size=load_size,
batch_size=batch_size,
serial_batches=serial_batches, nThreads=nThreads)
return data_loader
def CreateDataLoader(opt, k):
if k == 0:
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
print(data_loader.name())
data_loader.initialize(opt)
return data_loader
else:
from data.custom_dataset_data_loader_super import CustomDatasetDataLoader_super
data_loader = CustomDatasetDataLoader_super()
print(data_loader.name())
data_loader.initialize(opt)
return data_loader
def CreateDataLoader(
datafolder,
dataroot="./dataset",
dataset_mode="2afc",
load_size=64,
batch_size=1,
serial_batches=True,
):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
# print(data_loader.name())
data_loader.initialize(
datafolder,
dataroot=dataroot + "/" + dataset_mode,
dataset_mode=dataset_mode,
load_size=load_size,
batch_size=batch_size,
serial_batches=serial_batches,
nThreads=1,
)
return data_loader
def CreateDataLoader(opt):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
print(data_loader.name()) # The name returned is "CustomDatasetDataLoader"
data_loader.initialize(opt) # initialization parameters
return data_loader
def CreateDataLoader(opt):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
print(data_loader.name())
data_loader.initialize(opt)
return data_loader
def CreateDataLoader(opt, rank):
data_loader = CustomDatasetDataLoader()
# print(data_loader.name())
data_loader.initialize(opt, rank)
return data_loader
def main_task():
# define params
opt = BaseOptions().parse()
iter_path = os.path.join(opt.checkpoints_dir, 'iter.txt')
ioupath_path = os.path.join(opt.checkpoints_dir, 'MIoU.txt')
# load training data
if opt.continue_train:
try:
start_epoch, epoch_iter = np.loadtxt(iter_path,
delimiter=',',
dtype=int)
except:
start_epoch, epoch_iter = 1, 0
try:
best_iou = np.loadtxt(ioupath_path, dtype=float)
except:
best_iou = 0.
else:
start_epoch, epoch_iter = 1, 0
best_iou = 0.
os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu_ids[0])
# define data mode
data_loader = CustomDatasetDataLoader()
data_loader.initialize(opt)
dataset, dataset_val = data_loader.load_data()
dataset_size = len(dataset)
# define model
model = Deeplab_Solver(opt)
total_steps = (start_epoch - 1) * dataset_size + epoch_iter
print("starting training model......")
for epoch in range(start_epoch, opt.nepochs):
if epoch != start_epoch:
epoch_iter = epoch_iter % dataset_size
# for train
opt.isTrain = True
model.model.train()
for i, data in enumerate(dataset, start=epoch_iter):
total_steps += opt.batchSize
epoch_iter += opt.batchSize
# keep time to watch how times each one epoch
epoch_start_time = time.time()
# forward and backward pass
model.forward(data, isTrain=True)
model.backward(total_steps,
opt.nepochs * dataset_size * opt.batchSize + 1)
# save latest model
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest')
np.savetxt(iter_path, (epoch, epoch_iter),
delimiter=',',
fmt='%d')
if model.trainingavgloss < 0.010:
break
# for eval
opt.isTrain = False
model.model.eval()
if dataset_val != None:
label_trues, labels_preds = [], []
for i, data in enumerate(dataset_val):
seggt, segpred = model.forward(data, isTrain=False)
seggt = seggt.data.cpu().numpy()
segpred = segpred.data.cpu().numpy()
label_trues.append(seggt)
labels_preds.append(segpred)
metrics = util.label_accuracy_score(label_trues,
labels_preds,
n_class=opt.label_nc)
metrics *= 100
print('''\
Validation:
Accuracy: {0}
AccuracyClass: {1}
MeanIOU: {2}
FWAVAccuracy: {3}
'''.format(*metrics))
# save model for best
if metrics[2] > best_iou:
best_iou = metrics[2]
model.save('best')
print('end of epoch %d / %d \t Time Taken: %d sec' %
(epoch + 1, opt.nepochs, time.time() - epoch_start_time))
self.resize_or_crop = "resize_and_crop"
self.save_epoch_freq = 5
self.save_latest_freq = 5000
self.serial_batches = False
self.which_direction = "BtoA"
self.which_epoch = "latest"
self.checkpoints_dir = "/data/kdabi/CS698O/Autopainter/CS698-cartoon-painter/saved_models"
self.results_dir = "/data/kdabi/CS698O/Autopainter/CS698-cartoon-painter/saved_models"
opt = Options()
# opt = TrainOptions().parse()
data_loader = CustomDatasetDataLoader()
data_loader.initialize(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
# print('#training images = %d' % dataset_size)
model = FeatureLoss(opt)
visualizer = Visualizer(opt)
total_steps = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
total_steps += opt.batchSize
def CreateDataLoader(datafolder,dataroot='./dataset',dataset_mode='2afc',load_size=64,batch_size=1,serial_batches=True):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
# print(data_loader.name())
data_loader.initialize(datafolder,dataroot=dataroot+'/'+dataset_mode,dataset_mode=dataset_mode,load_size=load_size,batch_size=batch_size,serial_batches=serial_batches, nThreads=1)
return data_loader
def CreateDataLoader(opt, isVal=False):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
print(data_loader.name())
data_loader.initialize(opt, isVal)
return data_loader
def CreateDataLoader(config, filename):
from data.custom_dataset_data_loader import CustomDatasetDataLoader
data_loader = CustomDatasetDataLoader()
print(data_loader.name())
data_loader.initialize(config, filename)
return data_loader
def CreateDataLoader(opt):
data_loader = CustomDatasetDataLoader()
data_loader.initialize(opt)
return data_loader
